Figure5G

Load Data

dataset <- read.delim("raw_data/Figure5G.txt", stringsAsFactors = FALSE)

dataset$genotype <- gsub(" ", "", dataset$genotype )
dataset$genotype <- factor(dataset$genotype)
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))

dataset$siRNA <-  factor(gsub(".*[T,1]\\+","",dataset$genotype))
dataset$genotype <-  factor(gsub("\\+.*","",dataset$genotype))

dataset$UID <- factor(paste(dataset$Experiment, dataset$genotype, dataset$siRNA))
dataset$GSID <- factor(paste(dataset$genotype, dataset$siRNA))

# wide format
kable(dataset, row.names = F)

genotype	NT	olaparib_30nM	olaparib_300nM	olaparib_3000nM	Experiment	siRNA	UID	GSID
WT	2250	2050	1655	1022	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl
WT	1850	1600	1020	510	exp1	siBRCA1	exp1 WT siBRCA1	WT siBRCA1
YFP-ALC1	1926	1832	1676	1184	exp1	siCtrl	exp1 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl
YFP-ALC1	1700	1480	1180	1008	exp1	siBRCA1	exp1 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1
WT	2140	1920	1456	1105	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl
WT	1700	1460	910	560	exp2	siBRCA1	exp2 WT siBRCA1	WT siBRCA1
YFP-ALC1	1820	1790	1550	1223	exp2	siCtrl	exp2 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl
YFP-ALC1	1640	1420	1202	950	exp2	siBRCA1	exp2 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1
WT	3249	2741	2518	1774	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl
WT	4900	3680	2720	1776	exp3	siBRCA1	exp3 WT siBRCA1	WT siBRCA1
YFP-ALC1	4878	4872	3516	2984	exp3	siCtrl	exp3 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl
YFP-ALC1	3602	3404	2560	2200	exp3	siBRCA1	exp3 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1
WT	1992	1688	1404	910	exp4	siCtrl	exp4 WT siCtrl	WT siCtrl
WT	1880	1728	820	524	exp4	siBRCA1	exp4 WT siBRCA1	WT siBRCA1
YFP-ALC1	1040	920	706	564	exp4	siCtrl	exp4 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl
YFP-ALC1	452	392	300	245	exp4	siBRCA1	exp4 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1
WT	1960	1660	1510	820	exp5	siCtrl	exp5 WT siCtrl	WT siCtrl
WT	2066	1750	1011	556	exp5	siBRCA1	exp5 WT siBRCA1	WT siBRCA1
YFP-ALC1	1120	1012	856	632	exp5	siCtrl	exp5 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl
YFP-ALC1	552	495	352	330	exp5	siBRCA1	exp5 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1

library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")

dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$siRNA <- relevel(dataset$siRNA, ref = "siCtrl")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT siCtrl")

dataset$Olaparib <- gsub("NT","1",dataset$Treatment)
dataset$Olaparib <- gsub("olaparib_|nM","",dataset$Olaparib)
dataset$Olaparib <- log10(as.integer(dataset$Olaparib))




dataset$Offset <- NA
for(uid in levels(dataset$UID)){
        dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}

dataset$NormCounts <- dataset$Counts / dataset$Offset



dataset$Offset2 <- NA
for(gsid in levels(dataset$GSID)){
        dataset$Offset2[dataset$GSID == gsid] <- mean(dataset$NormCounts[dataset$GSID == gsid & dataset$Olaparib == 0])
}

dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2



# long format
kable(dataset, row.names = F)

genotype	Experiment	siRNA	UID	GSID	Treatment	Counts	Olaparib	Offset	NormCounts	Offset2	NormCounts2
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	NT	2250	0.000000	1744.25	1.2899527	1.298748	0.9932276
WT	exp1	siBRCA1	exp1 WT siBRCA1	WT siBRCA1	NT	1850	0.000000	1245.00	1.4859438	1.501467	0.9896610
YFP-ALC1	exp1	siCtrl	exp1 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	NT	1926	0.000000	1654.50	1.1640979	1.206172	0.9651176
YFP-ALC1	exp1	siBRCA1	exp1 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	NT	1700	0.000000	1342.00	1.2667660	1.265728	1.0008201
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	NT	2140	0.000000	1655.25	1.2928561	1.298748	0.9954631
WT	exp2	siBRCA1	exp2 WT siBRCA1	WT siBRCA1	NT	1700	0.000000	1157.50	1.4686825	1.501467	0.9781647
YFP-ALC1	exp2	siCtrl	exp2 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	NT	1820	0.000000	1595.75	1.1405295	1.206172	0.9455777
YFP-ALC1	exp2	siBRCA1	exp2 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	NT	1640	0.000000	1303.00	1.2586339	1.265728	0.9943953
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	NT	3249	0.000000	2570.50	1.2639564	1.298748	0.9732112
WT	exp3	siBRCA1	exp3 WT siBRCA1	WT siBRCA1	NT	4900	0.000000	3269.00	1.4989293	1.501467	0.9983096
YFP-ALC1	exp3	siCtrl	exp3 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	NT	4878	0.000000	4062.50	1.2007385	1.206172	0.9954951
YFP-ALC1	exp3	siBRCA1	exp3 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	NT	3602	0.000000	2941.50	1.2245453	1.265728	0.9674633
WT	exp4	siCtrl	exp4 WT siCtrl	WT siCtrl	NT	1992	0.000000	1498.50	1.3293293	1.298748	1.0235465
WT	exp4	siBRCA1	exp4 WT siBRCA1	WT siBRCA1	NT	1880	0.000000	1238.00	1.5185784	1.501467	1.0113961
YFP-ALC1	exp4	siCtrl	exp4 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	NT	1040	0.000000	807.50	1.2879257	1.206172	1.0677794
YFP-ALC1	exp4	siBRCA1	exp4 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	NT	452	0.000000	347.25	1.3016559	1.265728	1.0283852
WT	exp5	siCtrl	exp5 WT siCtrl	WT siCtrl	NT	1960	0.000000	1487.50	1.3176471	1.298748	1.0145515
WT	exp5	siBRCA1	exp5 WT siBRCA1	WT siBRCA1	NT	2066	0.000000	1345.75	1.5352034	1.501467	1.0224686
YFP-ALC1	exp5	siCtrl	exp5 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	NT	1120	0.000000	905.00	1.2375691	1.206172	1.0260302
YFP-ALC1	exp5	siBRCA1	exp5 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	NT	552	0.000000	432.25	1.2770388	1.265728	1.0089362
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	olaparib_30nM	2050	1.477121	1744.25	1.1752902	1.298748	0.9049407
WT	exp1	siBRCA1	exp1 WT siBRCA1	WT siBRCA1	olaparib_30nM	1600	1.477121	1245.00	1.2851406	1.501467	0.8559230
YFP-ALC1	exp1	siCtrl	exp1 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_30nM	1832	1.477121	1654.50	1.1072832	1.206172	0.9180142
YFP-ALC1	exp1	siBRCA1	exp1 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_30nM	1480	1.477121	1342.00	1.1028316	1.265728	0.8713022
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	olaparib_30nM	1920	1.477121	1655.25	1.1599456	1.298748	0.8931258
WT	exp2	siBRCA1	exp2 WT siBRCA1	WT siBRCA1	olaparib_30nM	1460	1.477121	1157.50	1.2613391	1.501467	0.8400709
YFP-ALC1	exp2	siCtrl	exp2 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_30nM	1790	1.477121	1595.75	1.1217296	1.206172	0.9299913
YFP-ALC1	exp2	siBRCA1	exp2 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_30nM	1420	1.477121	1303.00	1.0897928	1.265728	0.8610008
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	olaparib_30nM	2741	1.477121	2570.50	1.0663295	1.298748	0.8210440
WT	exp3	siBRCA1	exp3 WT siBRCA1	WT siBRCA1	olaparib_30nM	3680	1.477121	3269.00	1.1257265	1.501467	0.7497509
YFP-ALC1	exp3	siCtrl	exp3 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_30nM	4872	1.477121	4062.50	1.1992615	1.206172	0.9942706
YFP-ALC1	exp3	siBRCA1	exp3 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_30nM	3404	1.477121	2941.50	1.1572327	1.265728	0.9142823
WT	exp4	siCtrl	exp4 WT siCtrl	WT siCtrl	olaparib_30nM	1688	1.477121	1498.50	1.1264598	1.298748	0.8673426
WT	exp4	siBRCA1	exp4 WT siBRCA1	WT siBRCA1	olaparib_30nM	1728	1.477121	1238.00	1.3957997	1.501467	0.9296237
YFP-ALC1	exp4	siCtrl	exp4 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_30nM	920	1.477121	807.50	1.1393189	1.206172	0.9445740
YFP-ALC1	exp4	siBRCA1	exp4 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_30nM	392	1.477121	347.25	1.1288697	1.265728	0.8918739
WT	exp5	siCtrl	exp5 WT siCtrl	WT siCtrl	olaparib_30nM	1660	1.477121	1487.50	1.1159664	1.298748	0.8592630
WT	exp5	siBRCA1	exp5 WT siBRCA1	WT siBRCA1	olaparib_30nM	1750	1.477121	1345.75	1.3003901	1.501467	0.8660794
YFP-ALC1	exp5	siCtrl	exp5 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_30nM	1012	1.477121	905.00	1.1182320	1.206172	0.9270916
YFP-ALC1	exp5	siBRCA1	exp5 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_30nM	495	1.477121	432.25	1.1451706	1.265728	0.9047526
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	olaparib_300nM	1655	2.477121	1744.25	0.9488319	1.298748	0.7305741
WT	exp1	siBRCA1	exp1 WT siBRCA1	WT siBRCA1	olaparib_300nM	1020	2.477121	1245.00	0.8192771	1.501467	0.5456509
YFP-ALC1	exp1	siCtrl	exp1 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_300nM	1676	2.477121	1654.50	1.0129949	1.206172	0.8398427
YFP-ALC1	exp1	siBRCA1	exp1 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_300nM	1180	2.477121	1342.00	0.8792846	1.265728	0.6946869
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	olaparib_300nM	1456	2.477121	1655.25	0.8796254	1.298748	0.6772871
WT	exp2	siBRCA1	exp2 WT siBRCA1	WT siBRCA1	olaparib_300nM	910	2.477121	1157.50	0.7861771	1.501467	0.5236058
YFP-ALC1	exp2	siCtrl	exp2 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_300nM	1550	2.477121	1595.75	0.9713301	1.206172	0.8052997
YFP-ALC1	exp2	siBRCA1	exp2 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_300nM	1202	2.477121	1303.00	0.9224866	1.265728	0.7288190
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	olaparib_300nM	2518	2.477121	2570.50	0.9795760	1.298748	0.7542462
WT	exp3	siBRCA1	exp3 WT siBRCA1	WT siBRCA1	olaparib_300nM	2720	2.477121	3269.00	0.8320587	1.501467	0.5541637
YFP-ALC1	exp3	siCtrl	exp3 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_300nM	3516	2.477121	4062.50	0.8654769	1.206172	0.7175401
YFP-ALC1	exp3	siBRCA1	exp3 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_300nM	2560	2.477121	2941.50	0.8703043	1.265728	0.6875919
WT	exp4	siCtrl	exp4 WT siCtrl	WT siCtrl	olaparib_300nM	1404	2.477121	1498.50	0.9369369	1.298748	0.7214153
WT	exp4	siBRCA1	exp4 WT siBRCA1	WT siBRCA1	olaparib_300nM	820	2.477121	1238.00	0.6623586	1.501467	0.4411409
YFP-ALC1	exp4	siCtrl	exp4 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_300nM	706	2.477121	807.50	0.8743034	1.206172	0.7248579
YFP-ALC1	exp4	siBRCA1	exp4 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_300nM	300	2.477121	347.25	0.8639309	1.265728	0.6825565
WT	exp5	siCtrl	exp5 WT siCtrl	WT siCtrl	olaparib_300nM	1510	2.477121	1487.50	1.0151261	1.298748	0.7816188
WT	exp5	siBRCA1	exp5 WT siBRCA1	WT siBRCA1	olaparib_300nM	1011	2.477121	1345.75	0.7512539	1.501467	0.5003465
YFP-ALC1	exp5	siCtrl	exp5 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_300nM	856	2.477121	905.00	0.9458564	1.206172	0.7841802
YFP-ALC1	exp5	siBRCA1	exp5 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_300nM	352	2.477121	432.25	0.8143436	1.265728	0.6433796
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	olaparib_3000nM	1022	3.477121	1744.25	0.5859252	1.298748	0.4511461
WT	exp1	siBRCA1	exp1 WT siBRCA1	WT siBRCA1	olaparib_3000nM	510	3.477121	1245.00	0.4096386	1.501467	0.2728255
YFP-ALC1	exp1	siCtrl	exp1 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_3000nM	1184	3.477121	1654.50	0.7156241	1.206172	0.5933018
YFP-ALC1	exp1	siBRCA1	exp1 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_3000nM	1008	3.477121	1342.00	0.7511177	1.265728	0.5934275
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	olaparib_3000nM	1105	3.477121	1655.25	0.6675729	1.298748	0.5140125
WT	exp2	siBRCA1	exp2 WT siBRCA1	WT siBRCA1	olaparib_3000nM	560	3.477121	1157.50	0.4838013	1.501467	0.3222190
YFP-ALC1	exp2	siCtrl	exp2 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_3000nM	1223	3.477121	1595.75	0.7664108	1.206172	0.6354075
YFP-ALC1	exp2	siBRCA1	exp2 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_3000nM	950	3.477121	1303.00	0.7290867	1.265728	0.5760217
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	olaparib_3000nM	1774	3.477121	2570.50	0.6901381	1.298748	0.5313871
WT	exp3	siBRCA1	exp3 WT siBRCA1	WT siBRCA1	olaparib_3000nM	1776	3.477121	3269.00	0.5432854	1.501467	0.3618363
YFP-ALC1	exp3	siCtrl	exp3 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_3000nM	2984	3.477121	4062.50	0.7345231	1.206172	0.6089704
YFP-ALC1	exp3	siBRCA1	exp3 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_3000nM	2200	3.477121	2941.50	0.7479177	1.265728	0.5908993
WT	exp4	siCtrl	exp4 WT siCtrl	WT siCtrl	olaparib_3000nM	910	3.477121	1498.50	0.6072739	1.298748	0.4675840
WT	exp4	siBRCA1	exp4 WT siBRCA1	WT siBRCA1	olaparib_3000nM	524	3.477121	1238.00	0.4232633	1.501467	0.2818998
YFP-ALC1	exp4	siCtrl	exp4 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_3000nM	564	3.477121	807.50	0.6984520	1.206172	0.5790650
YFP-ALC1	exp4	siBRCA1	exp4 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_3000nM	245	3.477121	347.25	0.7055436	1.265728	0.5574212
WT	exp5	siCtrl	exp5 WT siCtrl	WT siCtrl	olaparib_3000nM	820	3.477121	1487.50	0.5512605	1.298748	0.4244552
WT	exp5	siBRCA1	exp5 WT siBRCA1	WT siBRCA1	olaparib_3000nM	556	3.477121	1345.75	0.4131525	1.501467	0.2751658
YFP-ALC1	exp5	siCtrl	exp5 YFP-ALC1 siCtrl	YFP-ALC1 siCtrl	olaparib_3000nM	632	3.477121	905.00	0.6983425	1.206172	0.5789742
YFP-ALC1	exp5	siBRCA1	exp5 YFP-ALC1 siBRCA1	YFP-ALC1 siBRCA1	olaparib_3000nM	330	3.477121	432.25	0.7634471	1.265728	0.6031684

Plot Data

library(ggplot2)

# raw data
ggplot(dataset, aes(x=Olaparib, y=Counts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=siRNA)) +
        geom_point(aes(colour=siRNA, shape=Experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_shape_manual(values=15:20) +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

library(Cairo)

cairo_pdf("Figure5G.pdf", width = 5, height = 4, family = "Arial")

ggplot(dataset, aes(x=Olaparib, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_point(aes(colour = siRNA, shape = genotype), size=1.75) +
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=TRUE, 
                    aes(group = GSID,colour = siRNA, linetype = genotype), fill='#DDDDDD', size=0.5) +
        #facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        ylab(label = "Normalized Counts") +
        scale_color_manual(values=c("#000000","#FF0000")) +
        guides(linetype = guide_legend(override.aes= list(color = "#555555"))) 

dev.off()

## quartz_off_screen 
##                 2

Models

library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)

Linear formula

fit1 <- lm(Counts ~ Experiment + Olaparib*siRNA*genotype, data = dataset)
print(summary(fit1))

## 
## Call:
## lm(formula = Counts ~ Experiment + Olaparib * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1060.56  -209.39    11.22   252.27  1391.68 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             2274.08     195.20  11.650  < 2e-16 ***
## Experimentexp2                           -68.56     152.59  -0.449  0.65462    
## Experimentexp3                          1714.44     152.59  11.236  < 2e-16 ***
## Experimentexp4                          -523.62     152.59  -3.432  0.00102 ** 
## Experimentexp5                          -453.81     152.59  -2.974  0.00406 ** 
## Olaparib                                -331.87      75.12  -4.418 3.67e-05 ***
## siRNAsiBRCA1                             174.68     239.95   0.728  0.46913    
## genotypeYFP-ALC1                        -141.46     239.95  -0.590  0.55746    
## Olaparib:siRNAsiBRCA1                   -169.46     106.23  -1.595  0.11531    
## Olaparib:genotypeYFP-ALC1                 83.60     106.23   0.787  0.43406    
## siRNAsiBRCA1:genotypeYFP-ALC1           -809.17     339.35  -2.384  0.01990 *  
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1   224.71     150.23   1.496  0.13935    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 431.6 on 68 degrees of freedom
## Multiple R-squared:  0.8508, Adjusted R-squared:  0.8267 
## F-statistic: 35.26 on 11 and 68 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit1))

## AIC:  1210.822

simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Olaparib*siRNA*genotype, data = dataset)
print(summary(fit2))

## 
## Call:
## lm(formula = NormCounts ~ Olaparib * siRNA * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.14430 -0.05874 -0.01280  0.05396  0.27693 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             1.35130    0.03274  41.277  < 2e-16 ***
## Olaparib                               -0.18909    0.01449 -13.047  < 2e-16 ***
## siRNAsiBRCA1                            0.22874    0.04630   4.941 4.90e-06 ***
## genotypeYFP-ALC1                       -0.09095    0.04630  -1.964 0.053343 .  
## Olaparib:siRNAsiBRCA1                  -0.12312    0.02050  -6.007 7.04e-08 ***
## Olaparib:genotypeYFP-ALC1               0.04895    0.02050   2.388 0.019548 *  
## siRNAsiBRCA1:genotypeYFP-ALC1          -0.19542    0.06547  -2.985 0.003875 ** 
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1  0.10519    0.02899   3.629 0.000529 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08327 on 72 degrees of freedom
## Multiple R-squared:  0.9216, Adjusted R-squared:  0.914 
## F-statistic:   121 on 7 and 72 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit2))

## AIC:  -161.1043

simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Olaparib*siRNA*genotype, data = dataset)
print(summary(fit3))

## 
## Call:
## lm(formula = NormCounts2 ~ Olaparib * siRNA * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.109758 -0.045560 -0.009451  0.041690  0.184443 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)                             1.040465   0.024147  43.088  < 2e-16
## Olaparib                               -0.145595   0.010690 -13.619  < 2e-16
## siRNAsiBRCA1                            0.011865   0.034149   0.347   0.7293
## genotypeYFP-ALC1                        0.004457   0.034149   0.131   0.8965
## Olaparib:siRNAsiBRCA1                  -0.062342   0.015119  -4.124 9.87e-05
## Olaparib:genotypeYFP-ALC1               0.029411   0.015119   1.945   0.0556
## siRNAsiBRCA1:genotypeYFP-ALC1          -0.034710   0.048295  -0.719   0.4746
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1  0.053641   0.021381   2.509   0.0144
##                                           
## (Intercept)                            ***
## Olaparib                               ***
## siRNAsiBRCA1                              
## genotypeYFP-ALC1                          
## Olaparib:siRNAsiBRCA1                  ***
## Olaparib:genotypeYFP-ALC1              .  
## siRNAsiBRCA1:genotypeYFP-ALC1             
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06142 on 72 degrees of freedom
## Multiple R-squared:  0.9256, Adjusted R-squared:  0.9183 
## F-statistic: 127.9 on 7 and 72 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit3))

## AIC:  -209.7982

simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Olaparib*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit4))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Olaparib * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 1103.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.32607 -0.48408  0.07435  0.38216  2.72754 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 944134   971.7   
##  Residual              77744   278.8   
## Number of obs: 80, groups:  UID, 20
## 
## Fixed effects:
##                                        Estimate Std. Error      df t value
## (Intercept)                             2407.76     448.16   17.37   5.373
## Olaparib                                -331.87      48.53   56.00  -6.838
## siRNAsiBRCA1                             174.68     633.79   17.37   0.276
## genotypeYFP-ALC1                        -141.46     633.79   17.37  -0.223
## Olaparib:siRNAsiBRCA1                   -169.46      68.63   56.00  -2.469
## Olaparib:genotypeYFP-ALC1                 83.60      68.63   56.00   1.218
## siRNAsiBRCA1:genotypeYFP-ALC1           -809.17     896.31   17.37  -0.903
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1   224.71      97.06   56.00   2.315
##                                        Pr(>|t|)    
## (Intercept)                            4.71e-05 ***
## Olaparib                               6.40e-09 ***
## siRNAsiBRCA1                             0.7861    
## genotypeYFP-ALC1                         0.8260    
## Olaparib:siRNAsiBRCA1                    0.0166 *  
## Olaparib:genotypeYFP-ALC1                0.2283    
## siRNAsiBRCA1:genotypeYFP-ALC1            0.3790    
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1   0.0243 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Olaprb sRNAsBRCA1 gYFP-A Ol:RNABRCA1 O:YFP- sRNABRCA1:
## Olaparib    -0.201                                                       
## siRNAsBRCA1 -0.707  0.142                                                
## gntYFP-ALC1 -0.707  0.142  0.500                                         
## Ol:RNABRCA1  0.142 -0.707 -0.201     -0.101                              
## Ol:YFP-ALC1  0.142 -0.707 -0.101     -0.201  0.500                       
## sRNABRCA1:Y  0.500 -0.101 -0.707     -0.707  0.142       0.142           
## O:RNABRCA1: -0.101  0.500  0.142      0.142 -0.707      -0.707 -0.201

cat("AIC: ", AIC(fit4))

## AIC:  1123.559

simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula

fit5 <- lm(Counts ~ Experiment + poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit5))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 2) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -946.66 -275.06   28.98  227.19 1267.54 
## 
## Coefficients:
##                                                  Estimate Std. Error t value
## (Intercept)                                       1657.51     137.15  12.085
## Experimentexp2                                     -68.56     153.34  -0.447
## Experimentexp3                                    1714.44     153.34  11.181
## Experimentexp4                                    -523.62     153.34  -3.415
## Experimentexp5                                    -453.81     153.34  -2.960
## poly(Olaparib, 2)1                               -3813.51     867.41  -4.396
## poly(Olaparib, 2)2                                -954.25     867.41  -1.100
## siRNAsiBRCA1                                      -140.15     137.15  -1.022
## genotypeYFP-ALC1                                    13.85     137.15   0.101
## poly(Olaparib, 2)1:siRNAsiBRCA1                  -1947.25    1226.70  -1.587
## poly(Olaparib, 2)2:siRNAsiBRCA1                    158.88    1226.70   0.130
## poly(Olaparib, 2)1:genotypeYFP-ALC1                960.61    1226.70   0.783
## poly(Olaparib, 2)2:genotypeYFP-ALC1                 18.02    1226.70   0.015
## siRNAsiBRCA1:genotypeYFP-ALC1                     -391.70     193.96  -2.020
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1  2582.11    1734.82   1.488
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1   476.17    1734.82   0.274
##                                                  Pr(>|t|)    
## (Intercept)                                       < 2e-16 ***
## Experimentexp2                                    0.65629    
## Experimentexp3                                    < 2e-16 ***
## Experimentexp4                                    0.00111 ** 
## Experimentexp5                                    0.00432 ** 
## poly(Olaparib, 2)1                               4.24e-05 ***
## poly(Olaparib, 2)2                                0.27540    
## siRNAsiBRCA1                                      0.31069    
## genotypeYFP-ALC1                                  0.91988    
## poly(Olaparib, 2)1:siRNAsiBRCA1                   0.11735    
## poly(Olaparib, 2)2:siRNAsiBRCA1                   0.89735    
## poly(Olaparib, 2)1:genotypeYFP-ALC1               0.43647    
## poly(Olaparib, 2)2:genotypeYFP-ALC1               0.98833    
## siRNAsiBRCA1:genotypeYFP-ALC1                     0.04763 *  
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1  0.14156    
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1  0.78460    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 433.7 on 64 degrees of freedom
## Multiple R-squared:  0.8582, Adjusted R-squared:  0.825 
## F-statistic: 25.83 on 15 and 64 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit5))

## AIC:  1214.757

simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit6))

## 
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.197464 -0.031413  0.000256  0.029071  0.197247 
## 
## Coefficients:
##                                                    Estimate Std. Error t value
## (Intercept)                                       1.000e+00  1.392e-02  71.815
## poly(Olaparib, 2)1                               -2.173e+00  1.245e-01 -17.446
## poly(Olaparib, 2)2                               -5.550e-01  1.245e-01  -4.456
## siRNAsiBRCA1                                      2.245e-16  1.969e-02   0.000
## genotypeYFP-ALC1                                  3.394e-16  1.969e-02   0.000
## poly(Olaparib, 2)1:siRNAsiBRCA1                  -1.415e+00  1.761e-01  -8.032
## poly(Olaparib, 2)2:siRNAsiBRCA1                  -4.597e-02  1.761e-01  -0.261
## poly(Olaparib, 2)1:genotypeYFP-ALC1               5.625e-01  1.761e-01   3.194
## poly(Olaparib, 2)2:genotypeYFP-ALC1               6.544e-02  1.761e-01   0.372
## siRNAsiBRCA1:genotypeYFP-ALC1                    -4.411e-16  2.785e-02   0.000
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1  1.209e+00  2.491e-01   4.852
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1  3.531e-01  2.491e-01   1.417
##                                                  Pr(>|t|)    
## (Intercept)                                       < 2e-16 ***
## poly(Olaparib, 2)1                                < 2e-16 ***
## poly(Olaparib, 2)2                               3.20e-05 ***
## siRNAsiBRCA1                                      1.00000    
## genotypeYFP-ALC1                                  1.00000    
## poly(Olaparib, 2)1:siRNAsiBRCA1                  1.91e-11 ***
## poly(Olaparib, 2)2:siRNAsiBRCA1                   0.79491    
## poly(Olaparib, 2)1:genotypeYFP-ALC1               0.00213 ** 
## poly(Olaparib, 2)2:genotypeYFP-ALC1               0.71141    
## siRNAsiBRCA1:genotypeYFP-ALC1                     1.00000    
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 7.45e-06 ***
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1  0.16094    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06227 on 68 degrees of freedom
## Multiple R-squared:  0.9586, Adjusted R-squared:  0.9519 
## F-statistic: 143.2 on 11 and 68 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit6))

## AIC:  -204.1673

simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit7))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.131514 -0.023050  0.000168  0.022818  0.131370 
## 
## Coefficients:
##                                                  Estimate Std. Error t value
## (Intercept)                                       0.76997    0.01013  76.015
## poly(Olaparib, 2)1                               -1.67303    0.09060 -18.466
## poly(Olaparib, 2)2                               -0.42734    0.09060  -4.717
## siRNAsiBRCA1                                     -0.10396    0.01432  -7.257
## genotypeYFP-ALC1                                  0.05910    0.01432   4.125
## poly(Olaparib, 2)1:siRNAsiBRCA1                  -0.71637    0.12813  -5.591
## poly(Olaparib, 2)2:siRNAsiBRCA1                   0.02708    0.12813   0.211
## poly(Olaparib, 2)1:genotypeYFP-ALC1               0.33796    0.12813   2.638
## poly(Olaparib, 2)2:genotypeYFP-ALC1               0.02145    0.12813   0.167
## siRNAsiBRCA1:genotypeYFP-ALC1                     0.06495    0.02026   3.206
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1  0.61639    0.18120   3.402
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1  0.23463    0.18120   1.295
##                                                  Pr(>|t|)    
## (Intercept)                                       < 2e-16 ***
## poly(Olaparib, 2)1                                < 2e-16 ***
## poly(Olaparib, 2)2                               1.23e-05 ***
## siRNAsiBRCA1                                     4.87e-10 ***
## genotypeYFP-ALC1                                 0.000103 ***
## poly(Olaparib, 2)1:siRNAsiBRCA1                  4.34e-07 ***
## poly(Olaparib, 2)2:siRNAsiBRCA1                  0.833222    
## poly(Olaparib, 2)1:genotypeYFP-ALC1              0.010337 *  
## poly(Olaparib, 2)2:genotypeYFP-ALC1              0.867528    
## siRNAsiBRCA1:genotypeYFP-ALC1                    0.002052 ** 
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 0.001125 ** 
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.199729    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0453 on 68 degrees of freedom
## Multiple R-squared:  0.9618, Adjusted R-squared:  0.9556 
## F-statistic: 155.5 on 11 and 68 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit7))

## AIC:  -255.0861

simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Olaparib,2)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit8))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 2) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 1017.8
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.07780 -0.36639 -0.01297  0.34311  3.02814 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 945657   972.4   
##  Residual              71655   267.7   
## Number of obs: 80, groups:  UID, 20
## 
## Fixed effects:
##                                                  Estimate Std. Error       df
## (Intercept)                                       1791.20     438.99    16.00
## poly(Olaparib, 2)1                               -3813.51     535.37    52.00
## poly(Olaparib, 2)2                                -954.25     535.37    52.00
## siRNAsiBRCA1                                      -140.15     620.83    16.00
## genotypeYFP-ALC1                                    13.85     620.83    16.00
## poly(Olaparib, 2)1:siRNAsiBRCA1                  -1947.25     757.13    52.00
## poly(Olaparib, 2)2:siRNAsiBRCA1                    158.88     757.13    52.00
## poly(Olaparib, 2)1:genotypeYFP-ALC1                960.61     757.13    52.00
## poly(Olaparib, 2)2:genotypeYFP-ALC1                 18.02     757.13    52.00
## siRNAsiBRCA1:genotypeYFP-ALC1                     -391.70     877.98    16.00
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1  2582.11    1070.74    52.00
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1   476.17    1070.74    52.00
##                                                  t value Pr(>|t|)    
## (Intercept)                                        4.080 0.000872 ***
## poly(Olaparib, 2)1                                -7.123 3.15e-09 ***
## poly(Olaparib, 2)2                                -1.782 0.080520 .  
## siRNAsiBRCA1                                      -0.226 0.824257    
## genotypeYFP-ALC1                                   0.022 0.982477    
## poly(Olaparib, 2)1:siRNAsiBRCA1                   -2.572 0.013012 *  
## poly(Olaparib, 2)2:siRNAsiBRCA1                    0.210 0.834608    
## poly(Olaparib, 2)1:genotypeYFP-ALC1                1.269 0.210179    
## poly(Olaparib, 2)2:genotypeYFP-ALC1                0.024 0.981108    
## siRNAsiBRCA1:genotypeYFP-ALC1                     -0.446 0.661478    
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1   2.412 0.019453 *  
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1   0.445 0.658373    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                   (Intr) pl(O,2)1 pl(O,2)2 sRNAsBRCA1 gYFP-A pl(O,2)1:RNABRCA1
## ply(Olp,2)1        0.000                                                      
## ply(Olp,2)2        0.000  0.000                                               
## siRNAsBRCA1       -0.707  0.000    0.000                                      
## gntYFP-ALC1       -0.707  0.000    0.000    0.500                             
## pl(O,2)1:RNABRCA1  0.000 -0.707    0.000    0.000      0.000                  
## pl(O,2)2:RNABRCA1  0.000  0.000   -0.707    0.000      0.000  0.000           
## p(O,2)1:YFP        0.000 -0.707    0.000    0.000      0.000  0.500           
## p(O,2)2:YFP        0.000  0.000   -0.707    0.000      0.000  0.000           
## sRNABRCA1:Y        0.500  0.000    0.000   -0.707     -0.707  0.000           
## p(O,2)1:RNABRCA1:  0.000  0.500    0.000    0.000      0.000 -0.707           
## p(O,2)2:RNABRCA1:  0.000  0.000    0.500    0.000      0.000  0.000           
##                   pl(O,2)2:RNABRCA1 p(O,2)1:Y p(O,2)2:Y sRNABRCA1:
## ply(Olp,2)1                                                       
## ply(Olp,2)2                                                       
## siRNAsBRCA1                                                       
## gntYFP-ALC1                                                       
## pl(O,2)1:RNABRCA1                                                 
## pl(O,2)2:RNABRCA1                                                 
## p(O,2)1:YFP        0.000                                          
## p(O,2)2:YFP        0.500             0.000                        
## sRNABRCA1:Y        0.000             0.000     0.000              
## p(O,2)1:RNABRCA1:  0.000            -0.707     0.000     0.000    
## p(O,2)2:RNABRCA1: -0.707             0.000    -0.707     0.000    
##                   p(O,2)1:RNABRCA1:
## ply(Olp,2)1                        
## ply(Olp,2)2                        
## siRNAsBRCA1                        
## gntYFP-ALC1                        
## pl(O,2)1:RNABRCA1                  
## pl(O,2)2:RNABRCA1                  
## p(O,2)1:YFP                        
## p(O,2)2:YFP                        
## sRNABRCA1:Y                        
## p(O,2)1:RNABRCA1:                  
## p(O,2)2:RNABRCA1:  0.000

cat("AIC: ", AIC(fit8))

## AIC:  1045.844

simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula

fit9 <- lm(Counts ~ Experiment + poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit9))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 3) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -932.95 -192.29   42.57  237.90 1206.05 
## 
## Coefficients:
##                                                  Estimate Std. Error t value
## (Intercept)                                       1657.51     140.33  11.811
## Experimentexp2                                     -68.56     156.90  -0.437
## Experimentexp3                                    1714.44     156.90  10.927
## Experimentexp4                                    -523.62     156.90  -3.337
## Experimentexp5                                    -453.81     156.90  -2.892
## poly(Olaparib, 3)1                               -3813.51     887.54  -4.297
## poly(Olaparib, 3)2                                -954.25     887.54  -1.075
## poly(Olaparib, 3)3                                -235.30     887.54  -0.265
## siRNAsiBRCA1                                      -140.15     140.33  -0.999
## genotypeYFP-ALC1                                    13.85     140.33   0.099
## poly(Olaparib, 3)1:siRNAsiBRCA1                  -1947.25    1255.17  -1.551
## poly(Olaparib, 3)2:siRNAsiBRCA1                    158.88    1255.17   0.127
## poly(Olaparib, 3)3:siRNAsiBRCA1                    936.68    1255.17   0.746
## poly(Olaparib, 3)1:genotypeYFP-ALC1                960.61    1255.17   0.765
## poly(Olaparib, 3)2:genotypeYFP-ALC1                 18.02    1255.17   0.014
## poly(Olaparib, 3)3:genotypeYFP-ALC1                683.49    1255.17   0.545
## siRNAsiBRCA1:genotypeYFP-ALC1                     -391.70     198.46  -1.974
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1  2582.11    1775.08   1.455
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1   476.17    1775.08   0.268
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -1008.92    1775.08  -0.568
##                                                  Pr(>|t|)    
## (Intercept)                                       < 2e-16 ***
## Experimentexp2                                    0.66369    
## Experimentexp3                                   6.69e-16 ***
## Experimentexp4                                    0.00146 ** 
## Experimentexp5                                    0.00532 ** 
## poly(Olaparib, 3)1                               6.45e-05 ***
## poly(Olaparib, 3)2                                0.28661    
## poly(Olaparib, 3)3                                0.79183    
## siRNAsiBRCA1                                      0.32195    
## genotypeYFP-ALC1                                  0.92171    
## poly(Olaparib, 3)1:siRNAsiBRCA1                   0.12607    
## poly(Olaparib, 3)2:siRNAsiBRCA1                   0.89970    
## poly(Olaparib, 3)3:siRNAsiBRCA1                   0.45843    
## poly(Olaparib, 3)1:genotypeYFP-ALC1               0.44708    
## poly(Olaparib, 3)2:genotypeYFP-ALC1               0.98860    
## poly(Olaparib, 3)3:genotypeYFP-ALC1               0.58809    
## siRNAsiBRCA1:genotypeYFP-ALC1                     0.05303 .  
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1  0.15098    
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1  0.78943    
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1  0.57190    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 443.8 on 60 degrees of freedom
## Multiple R-squared:  0.8608, Adjusted R-squared:  0.8168 
## F-statistic: 19.53 on 19 and 60 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit9))

## AIC:  1221.265

simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit10))

## 
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.147953 -0.025716 -0.002438  0.029525  0.122120 
## 
## Coefficients:
##                                                    Estimate Std. Error t value
## (Intercept)                                       1.000e+00  1.113e-02  89.848
## poly(Olaparib, 3)1                               -2.173e+00  9.955e-02 -21.827
## poly(Olaparib, 3)2                               -5.550e-01  9.955e-02  -5.575
## poly(Olaparib, 3)3                               -1.224e-01  9.955e-02  -1.229
## siRNAsiBRCA1                                      4.100e-16  1.574e-02   0.000
## genotypeYFP-ALC1                                  2.941e-16  1.574e-02   0.000
## poly(Olaparib, 3)1:siRNAsiBRCA1                  -1.415e+00  1.408e-01 -10.049
## poly(Olaparib, 3)2:siRNAsiBRCA1                  -4.597e-02  1.408e-01  -0.326
## poly(Olaparib, 3)3:siRNAsiBRCA1                   6.700e-01  1.408e-01   4.759
## poly(Olaparib, 3)1:genotypeYFP-ALC1               5.625e-01  1.408e-01   3.996
## poly(Olaparib, 3)2:genotypeYFP-ALC1               6.544e-02  1.408e-01   0.465
## poly(Olaparib, 3)3:genotypeYFP-ALC1               2.601e-01  1.408e-01   1.847
## siRNAsiBRCA1:genotypeYFP-ALC1                    -3.152e-16  2.226e-02   0.000
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1  1.209e+00  1.991e-01   6.071
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1  3.531e-01  1.991e-01   1.773
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -5.132e-01  1.991e-01  -2.578
##                                                  Pr(>|t|)    
## (Intercept)                                       < 2e-16 ***
## poly(Olaparib, 3)1                                < 2e-16 ***
## poly(Olaparib, 3)2                               5.32e-07 ***
## poly(Olaparib, 3)3                               0.223550    
## siRNAsiBRCA1                                     1.000000    
## genotypeYFP-ALC1                                 1.000000    
## poly(Olaparib, 3)1:siRNAsiBRCA1                  8.69e-15 ***
## poly(Olaparib, 3)2:siRNAsiBRCA1                  0.745112    
## poly(Olaparib, 3)3:siRNAsiBRCA1                  1.15e-05 ***
## poly(Olaparib, 3)1:genotypeYFP-ALC1              0.000169 ***
## poly(Olaparib, 3)2:genotypeYFP-ALC1              0.643648    
## poly(Olaparib, 3)3:genotypeYFP-ALC1              0.069306 .  
## siRNAsiBRCA1:genotypeYFP-ALC1                    1.000000    
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 7.66e-08 ***
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.080940 .  
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 0.012260 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04977 on 64 degrees of freedom
## Multiple R-squared:  0.9751, Adjusted R-squared:  0.9693 
## F-statistic: 167.2 on 15 and 64 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit10))

## AIC:  -236.8624

simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit11))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.098539 -0.020350 -0.001746  0.021531  0.081334 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                       0.769972   0.008257  93.251
## poly(Olaparib, 3)1                               -1.673026   0.073853 -22.654
## poly(Olaparib, 3)2                               -0.427341   0.073853  -5.786
## poly(Olaparib, 3)3                               -0.094207   0.073853  -1.276
## siRNAsiBRCA1                                     -0.103957   0.011677  -8.903
## genotypeYFP-ALC1                                  0.059097   0.011677   5.061
## poly(Olaparib, 3)1:siRNAsiBRCA1                  -0.716369   0.104443  -6.859
## poly(Olaparib, 3)2:siRNAsiBRCA1                   0.027083   0.104443   0.259
## poly(Olaparib, 3)3:siRNAsiBRCA1                   0.458933   0.104443   4.394
## poly(Olaparib, 3)1:genotypeYFP-ALC1               0.337956   0.104443   3.236
## poly(Olaparib, 3)2:genotypeYFP-ALC1               0.021452   0.104443   0.205
## poly(Olaparib, 3)3:genotypeYFP-ALC1               0.208401   0.104443   1.995
## siRNAsiBRCA1:genotypeYFP-ALC1                     0.064947   0.016514   3.933
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1  0.616391   0.147705   4.173
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1  0.234634   0.147705   1.589
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -0.340450   0.147705  -2.305
##                                                  Pr(>|t|)    
## (Intercept)                                       < 2e-16 ***
## poly(Olaparib, 3)1                                < 2e-16 ***
## poly(Olaparib, 3)2                               2.34e-07 ***
## poly(Olaparib, 3)3                               0.206705    
## siRNAsiBRCA1                                     8.37e-13 ***
## genotypeYFP-ALC1                                 3.77e-06 ***
## poly(Olaparib, 3)1:siRNAsiBRCA1                  3.29e-09 ***
## poly(Olaparib, 3)2:siRNAsiBRCA1                  0.796227    
## poly(Olaparib, 3)3:siRNAsiBRCA1                  4.27e-05 ***
## poly(Olaparib, 3)1:genotypeYFP-ALC1              0.001922 ** 
## poly(Olaparib, 3)2:genotypeYFP-ALC1              0.837915    
## poly(Olaparib, 3)3:genotypeYFP-ALC1              0.050266 .  
## siRNAsiBRCA1:genotypeYFP-ALC1                    0.000209 ***
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 9.24e-05 ***
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.117095    
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 0.024424 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03693 on 64 degrees of freedom
## Multiple R-squared:  0.9761, Adjusted R-squared:  0.9705 
## F-statistic: 174.2 on 15 and 64 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit11))

## AIC:  -284.6344

simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Olaparib,3)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit12))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 3) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 957.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.20787 -0.42360 -0.02922  0.33628  3.08502 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 945322   972.3   
##  Residual              72994   270.2   
## Number of obs: 80, groups:  UID, 20
## 
## Fixed effects:
##                                                  Estimate Std. Error       df
## (Intercept)                                       1791.20     438.99    16.00
## poly(Olaparib, 3)1                               -3813.51     540.35    48.00
## poly(Olaparib, 3)2                                -954.25     540.35    48.00
## poly(Olaparib, 3)3                                -235.30     540.35    48.00
## siRNAsiBRCA1                                      -140.15     620.83    16.00
## genotypeYFP-ALC1                                    13.85     620.83    16.00
## poly(Olaparib, 3)1:siRNAsiBRCA1                  -1947.25     764.17    48.00
## poly(Olaparib, 3)2:siRNAsiBRCA1                    158.88     764.17    48.00
## poly(Olaparib, 3)3:siRNAsiBRCA1                    936.68     764.17    48.00
## poly(Olaparib, 3)1:genotypeYFP-ALC1                960.61     764.17    48.00
## poly(Olaparib, 3)2:genotypeYFP-ALC1                 18.02     764.17    48.00
## poly(Olaparib, 3)3:genotypeYFP-ALC1                683.49     764.17    48.00
## siRNAsiBRCA1:genotypeYFP-ALC1                     -391.70     877.98    16.00
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1  2582.11    1080.69    48.00
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1   476.17    1080.69    48.00
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -1008.92    1080.69    48.00
##                                                  t value Pr(>|t|)    
## (Intercept)                                        4.080 0.000872 ***
## poly(Olaparib, 3)1                                -7.058    6e-09 ***
## poly(Olaparib, 3)2                                -1.766 0.083756 .  
## poly(Olaparib, 3)3                                -0.435 0.665177    
## siRNAsiBRCA1                                      -0.226 0.824257    
## genotypeYFP-ALC1                                   0.022 0.982477    
## poly(Olaparib, 3)1:siRNAsiBRCA1                   -2.548 0.014083 *  
## poly(Olaparib, 3)2:siRNAsiBRCA1                    0.208 0.836177    
## poly(Olaparib, 3)3:siRNAsiBRCA1                    1.226 0.226271    
## poly(Olaparib, 3)1:genotypeYFP-ALC1                1.257 0.214811    
## poly(Olaparib, 3)2:genotypeYFP-ALC1                0.024 0.981289    
## poly(Olaparib, 3)3:genotypeYFP-ALC1                0.894 0.375558    
## siRNAsiBRCA1:genotypeYFP-ALC1                     -0.446 0.661478    
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1   2.389 0.020858 *  
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1   0.441 0.661469    
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1  -0.934 0.355191    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cat("AIC: ", AIC(fit12))

## AIC:  993.1168

simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results

ICtab(fit1,fit2,fit3,fit4,
      fit5,fit6,fit7,fit8,
      fit9,fit10,fit11,fit12,
      base=T)

##       AIC    dAIC   df
## fit11 -284.6    0.0 17
## fit7  -255.1   29.5 13
## fit10 -236.9   47.8 17
## fit3  -209.8   74.8 9 
## fit6  -204.2   80.5 13
## fit2  -161.1  123.5 9 
## fit12  993.1 1277.8 18
## fit8  1045.8 1330.5 14
## fit4  1123.6 1408.2 10
## fit1  1210.8 1495.5 13
## fit5  1214.8 1499.4 17
## fit9  1221.3 1505.9 21

Final Result

fit <- fit11

output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in WT siCtrl	-1.6730264	0.0738526	-22.6536025	0.0000000
Olaparib2 in WT siCtrl	-0.4273414	0.0738526	-5.7864132	0.0000002
Olaparib3 in WT siCtrl	-0.0942069	0.0738526	-1.2756078	0.2067048
Olaparib1: siCtrl vs. siBRCA1 in WT	-0.7163693	0.1044433	-6.8589315	0.0000000
Olaparib2: siCtrl vs. siBRCA1 in WT	0.0270834	0.1044433	0.2593116	0.7962271
Olaparib3: siCtrl vs. siBRCA1 in WT	0.4589328	0.1044433	4.3940864	0.0000427
Olaparib1: WT vs. YFP-ALC1 in siCtrl	0.3379562	0.1044433	3.2357872	0.0019220
Olaparib2: WT vs. YFP-ALC1 in siCtrl	0.0214522	0.1044433	0.2053957	0.8379149
Olaparib3: WT vs. YFP-ALC1 in siCtrl	0.2084006	0.1044433	1.9953475	0.0502657
Olaparib1: siCtrl vs. siBRCA1: WT vs. YFP-ALC1	0.6163909	0.1477051	4.1731182	0.0000924
Olaparib2: siCtrl vs. siBRCA1: WT vs. YFP-ALC1	0.2346342	0.1477051	1.5885316	0.1170946
Olaparib3: siCtrl vs. siBRCA1: WT vs. YFP-ALC1	-0.3404500	0.1477051	-2.3049303	0.0244245

write.table(output, file = "Figure5G_Stats_Ref_WT_siCtrl.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

# re-fit with WT siBRCA1 reference
dataset$siRNA <- relevel(dataset$siRNA, ref = "siBRCA1")
 
fit <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)

output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in WT siBRCA1	-2.3893957	0.0738526	-32.3535964	0.0000000
Olaparib2 in WT siBRCA1	-0.4002580	0.0738526	-5.4196913	0.0000010
Olaparib3 in WT siBRCA1	0.3647259	0.0738526	4.9385688	0.0000059
Olaparib1: siBRCA1 vs. siCtrl in WT	0.7163693	0.1044433	6.8589315	0.0000000
Olaparib2: siBRCA1 vs. siCtrl in WT	-0.0270834	0.1044433	-0.2593116	0.7962271
Olaparib3: siBRCA1 vs. siCtrl in WT	-0.4589328	0.1044433	-4.3940864	0.0000427
Olaparib1: WT vs. YFP-ALC1 in siBRCA1	0.9543471	0.1044433	9.1374676	0.0000000
Olaparib2: WT vs. YFP-ALC1 in siBRCA1	0.2560864	0.1044433	2.4519186	0.0169487
Olaparib3: WT vs. YFP-ALC1 in siBRCA1	-0.1320493	0.1044433	-1.2643162	0.2107017
Olaparib1: siBRCA1 vs. siCtrl: WT vs. YFP-ALC1	-0.6163909	0.1477051	-4.1731182	0.0000924
Olaparib2: siBRCA1 vs. siCtrl: WT vs. YFP-ALC1	-0.2346342	0.1477051	-1.5885316	0.1170946
Olaparib3: siBRCA1 vs. siCtrl: WT vs. YFP-ALC1	0.3404500	0.1477051	2.3049303	0.0244245

write.table(output, file = "Figure5G_Stats_Ref_WT_siBRCA1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

# re-fit with YFP-ALC1 siBRCA1 reference
dataset$genotype <- relevel(dataset$genotype, ref = "YFP-ALC1")
dataset$siRNA <- relevel(dataset$siRNA, ref = "siBRCA1")
 
fit <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)

output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in YFP-ALC1 siBRCA1	-1.4350486	0.0738526	-19.4312659	0.0000000
Olaparib2 in YFP-ALC1 siBRCA1	-0.1441716	0.0738526	-1.9521547	0.0552975
Olaparib3 in YFP-ALC1 siBRCA1	0.2326766	0.0738526	3.1505557	0.0024781
Olaparib1: siBRCA1 vs. siCtrl in YFP-ALC1	0.0999785	0.1044433	0.9572511	0.3420427
Olaparib2: siBRCA1 vs. siCtrl in YFP-ALC1	-0.2617176	0.1044433	-2.5058345	0.0147700
Olaparib3: siBRCA1 vs. siCtrl in YFP-ALC1	-0.1184828	0.1044433	-1.1344227	0.2608484
Olaparib1: YFP-ALC1 vs. WT in siBRCA1	-0.9543471	0.1044433	-9.1374676	0.0000000
Olaparib2: YFP-ALC1 vs. WT in siBRCA1	-0.2560864	0.1044433	-2.4519186	0.0169487
Olaparib3: YFP-ALC1 vs. WT in siBRCA1	0.1320493	0.1044433	1.2643162	0.2107017
Olaparib1: siBRCA1 vs. siCtrl: YFP-ALC1 vs. WT	0.6163909	0.1477051	4.1731182	0.0000924
Olaparib2: siBRCA1 vs. siCtrl: YFP-ALC1 vs. WT	0.2346342	0.1477051	1.5885316	0.1170946
Olaparib3: siBRCA1 vs. siCtrl: YFP-ALC1 vs. WT	-0.3404500	0.1477051	-2.3049303	0.0244245

write.table(output, file = "Figure5G_Stats_Ref_YFP-ALC1_siBRCA1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

Anova

fit11a <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
fit11b <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA+genotype, data = dataset)

# anova table
anova(fit11a, fit11b)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Olaparib, 3) * siRNA * genotype
## Model 2: NormCounts2 ~ poly(Olaparib, 3) * siRNA + genotype
##   Res.Df      RSS Df Sum of Sq      F    Pr(>F)    
## 1     64 0.087267                                  
## 2     71 0.252346 -7  -0.16508 17.295 1.256e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

fit11c <- lm(NormCounts2 ~ poly(Olaparib,3)*genotype*siRNA, data = dataset)
fit11d <- lm(NormCounts2 ~ poly(Olaparib,3)*genotype+siRNA, data = dataset)

# anova table
anova(fit11c, fit11d)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Olaparib, 3) * genotype * siRNA
## Model 2: NormCounts2 ~ poly(Olaparib, 3) * genotype + siRNA
##   Res.Df      RSS Df Sum of Sq     F    Pr(>F)    
## 1     64 0.087267                                 
## 2     71 0.210491 -7  -0.12322 12.91 3.197e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

New analysis

• fit model for groups (genotype+siRNA)

dataset$GSID <- factor(dataset$GSID, levels = unique(dataset$GSID))

GSID_levels <- levels(dataset$GSID)

for(g in seq_along(GSID_levels)){
        
        cat("Group: ", GSID_levels[g], "\n")
        
        dataset$GSID <- factor(dataset$GSID, levels = unique(dataset$GSID))
        dataset$GSID <- relevel(dataset$GSID, ref = GSID_levels[g])
        
        fit_group <- lm(NormCounts2 ~ poly(Olaparib,3)*GSID, data = dataset)
        
        print(summary(fit_group))
        cat("AIC: ", AIC(fit_group))
        
        
        simres <- simulateResiduals(fittedModel = fit_group)
        plot(simres)
        
        if(g == 1){
                output <- coef(summary(fit_group))
                output <- output[grep("Olaparib", rownames(output)),]
                rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output))
                
                rownames(output) <- gsub("GSID",  paste0(" ",GSID_levels[g], " vs. "), rownames(output))
                rownames(output)[!(grepl("vs", rownames(output)))]  <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$GSID)[1],  sep = " in " )

                
        } else {
                outtmp <- coef(summary(fit_group))
                outtmp <- outtmp[grep("Olaparib", rownames(outtmp)),]
                rownames(outtmp) <- gsub("poly\\(|, [1-3]\\)","", rownames(outtmp))
                
                rownames(outtmp) <- gsub("GSID",  paste0(" ",GSID_levels[g], " vs. "), rownames(outtmp))
                rownames(outtmp)[!(grepl("vs", rownames(outtmp)))]  <- paste(rownames(outtmp)[!(grepl("vs", rownames(outtmp)))], levels(dataset$GSID)[1],  sep = " in " )

                
                output <- rbind(output,outtmp)
        }
        
        if(g < length(GSID_levels)){
                
                output <- rbind(output, " ", colnames(output))
        }
        
}

## Group:  WT siCtrl 
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.098539 -0.020350 -0.001746  0.021531  0.081334 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              0.769972   0.008257  93.251  < 2e-16
## poly(Olaparib, 3)1                      -1.673026   0.073853 -22.654  < 2e-16
## poly(Olaparib, 3)2                      -0.427341   0.073853  -5.786 2.34e-07
## poly(Olaparib, 3)3                      -0.094207   0.073853  -1.276  0.20670
## GSIDWT siBRCA1                          -0.103957   0.011677  -8.903 8.37e-13
## GSIDYFP-ALC1 siCtrl                      0.059097   0.011677   5.061 3.77e-06
## GSIDYFP-ALC1 siBRCA1                     0.020087   0.011677   1.720  0.09023
## poly(Olaparib, 3)1:GSIDWT siBRCA1       -0.716369   0.104443  -6.859 3.29e-09
## poly(Olaparib, 3)2:GSIDWT siBRCA1        0.027083   0.104443   0.259  0.79623
## poly(Olaparib, 3)3:GSIDWT siBRCA1        0.458933   0.104443   4.394 4.27e-05
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siCtrl   0.337956   0.104443   3.236  0.00192
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siCtrl   0.021452   0.104443   0.205  0.83791
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siCtrl   0.208401   0.104443   1.995  0.05027
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siBRCA1  0.237978   0.104443   2.279  0.02604
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siBRCA1  0.283170   0.104443   2.711  0.00860
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siBRCA1  0.326883   0.104443   3.130  0.00263
##                                            
## (Intercept)                             ***
## poly(Olaparib, 3)1                      ***
## poly(Olaparib, 3)2                      ***
## poly(Olaparib, 3)3                         
## GSIDWT siBRCA1                          ***
## GSIDYFP-ALC1 siCtrl                     ***
## GSIDYFP-ALC1 siBRCA1                    .  
## poly(Olaparib, 3)1:GSIDWT siBRCA1       ***
## poly(Olaparib, 3)2:GSIDWT siBRCA1          
## poly(Olaparib, 3)3:GSIDWT siBRCA1       ***
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siCtrl  ** 
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siCtrl     
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siCtrl  .  
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siBRCA1 *  
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siBRCA1 ** 
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siBRCA1 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03693 on 64 degrees of freedom
## Multiple R-squared:  0.9761, Adjusted R-squared:  0.9705 
## F-statistic: 174.2 on 15 and 64 DF,  p-value: < 2.2e-16
## 
## AIC:  -284.6344

## Group:  WT siBRCA1 
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.098539 -0.020350 -0.001746  0.021531  0.081334 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              0.666015   0.008257  80.661  < 2e-16
## poly(Olaparib, 3)1                      -2.389396   0.073853 -32.354  < 2e-16
## poly(Olaparib, 3)2                      -0.400258   0.073853  -5.420 9.67e-07
## poly(Olaparib, 3)3                       0.364726   0.073853   4.939 5.94e-06
## GSIDWT siCtrl                            0.103957   0.011677   8.903 8.37e-13
## GSIDYFP-ALC1 siCtrl                      0.163054   0.011677  13.964  < 2e-16
## GSIDYFP-ALC1 siBRCA1                     0.124044   0.011677  10.623 9.24e-16
## poly(Olaparib, 3)1:GSIDWT siCtrl         0.716369   0.104443   6.859 3.29e-09
## poly(Olaparib, 3)2:GSIDWT siCtrl        -0.027083   0.104443  -0.259   0.7962
## poly(Olaparib, 3)3:GSIDWT siCtrl        -0.458933   0.104443  -4.394 4.27e-05
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siCtrl   1.054326   0.104443  10.095 7.27e-15
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siCtrl  -0.005631   0.104443  -0.054   0.9572
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siCtrl  -0.250532   0.104443  -2.399   0.0194
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siBRCA1  0.954347   0.104443   9.137 3.26e-13
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siBRCA1  0.256086   0.104443   2.452   0.0169
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siBRCA1 -0.132049   0.104443  -1.264   0.2107
##                                            
## (Intercept)                             ***
## poly(Olaparib, 3)1                      ***
## poly(Olaparib, 3)2                      ***
## poly(Olaparib, 3)3                      ***
## GSIDWT siCtrl                           ***
## GSIDYFP-ALC1 siCtrl                     ***
## GSIDYFP-ALC1 siBRCA1                    ***
## poly(Olaparib, 3)1:GSIDWT siCtrl        ***
## poly(Olaparib, 3)2:GSIDWT siCtrl           
## poly(Olaparib, 3)3:GSIDWT siCtrl        ***
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siCtrl  ***
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siCtrl     
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siCtrl  *  
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siBRCA1 ***
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siBRCA1 *  
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siBRCA1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03693 on 64 degrees of freedom
## Multiple R-squared:  0.9761, Adjusted R-squared:  0.9705 
## F-statistic: 174.2 on 15 and 64 DF,  p-value: < 2.2e-16
## 
## AIC:  -284.6344

## Group:  YFP-ALC1 siCtrl 
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.098539 -0.020350 -0.001746  0.021531  0.081334 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              0.829069   0.008257 100.408  < 2e-16
## poly(Olaparib, 3)1                      -1.335070   0.073853 -18.078  < 2e-16
## poly(Olaparib, 3)2                      -0.405889   0.073853  -5.496 7.22e-07
## poly(Olaparib, 3)3                       0.114194   0.073853   1.546  0.12698
## GSIDWT siCtrl                           -0.059097   0.011677  -5.061 3.77e-06
## GSIDWT siBRCA1                          -0.163054   0.011677 -13.964  < 2e-16
## GSIDYFP-ALC1 siBRCA1                    -0.039010   0.011677  -3.341  0.00140
## poly(Olaparib, 3)1:GSIDWT siCtrl        -0.337956   0.104443  -3.236  0.00192
## poly(Olaparib, 3)2:GSIDWT siCtrl        -0.021452   0.104443  -0.205  0.83791
## poly(Olaparib, 3)3:GSIDWT siCtrl        -0.208401   0.104443  -1.995  0.05027
## poly(Olaparib, 3)1:GSIDWT siBRCA1       -1.054326   0.104443 -10.095 7.27e-15
## poly(Olaparib, 3)2:GSIDWT siBRCA1        0.005631   0.104443   0.054  0.95717
## poly(Olaparib, 3)3:GSIDWT siBRCA1        0.250532   0.104443   2.399  0.01938
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siBRCA1 -0.099978   0.104443  -0.957  0.34204
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siBRCA1  0.261718   0.104443   2.506  0.01477
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siBRCA1  0.118483   0.104443   1.134  0.26085
##                                            
## (Intercept)                             ***
## poly(Olaparib, 3)1                      ***
## poly(Olaparib, 3)2                      ***
## poly(Olaparib, 3)3                         
## GSIDWT siCtrl                           ***
## GSIDWT siBRCA1                          ***
## GSIDYFP-ALC1 siBRCA1                    ** 
## poly(Olaparib, 3)1:GSIDWT siCtrl        ** 
## poly(Olaparib, 3)2:GSIDWT siCtrl           
## poly(Olaparib, 3)3:GSIDWT siCtrl        .  
## poly(Olaparib, 3)1:GSIDWT siBRCA1       ***
## poly(Olaparib, 3)2:GSIDWT siBRCA1          
## poly(Olaparib, 3)3:GSIDWT siBRCA1       *  
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siBRCA1    
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siBRCA1 *  
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siBRCA1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03693 on 64 degrees of freedom
## Multiple R-squared:  0.9761, Adjusted R-squared:  0.9705 
## F-statistic: 174.2 on 15 and 64 DF,  p-value: < 2.2e-16
## 
## AIC:  -284.6344

## Group:  YFP-ALC1 siBRCA1 
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.098539 -0.020350 -0.001746  0.021531  0.081334 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)                             0.790059   0.008257  95.684  < 2e-16
## poly(Olaparib, 3)1                     -1.435049   0.073853 -19.431  < 2e-16
## poly(Olaparib, 3)2                     -0.144172   0.073853  -1.952  0.05530
## poly(Olaparib, 3)3                      0.232677   0.073853   3.151  0.00248
## GSIDWT siCtrl                          -0.020087   0.011677  -1.720  0.09023
## GSIDWT siBRCA1                         -0.124044   0.011677 -10.623 9.24e-16
## GSIDYFP-ALC1 siCtrl                     0.039010   0.011677   3.341  0.00140
## poly(Olaparib, 3)1:GSIDWT siCtrl       -0.237978   0.104443  -2.279  0.02604
## poly(Olaparib, 3)2:GSIDWT siCtrl       -0.283170   0.104443  -2.711  0.00860
## poly(Olaparib, 3)3:GSIDWT siCtrl       -0.326883   0.104443  -3.130  0.00263
## poly(Olaparib, 3)1:GSIDWT siBRCA1      -0.954347   0.104443  -9.137 3.26e-13
## poly(Olaparib, 3)2:GSIDWT siBRCA1      -0.256086   0.104443  -2.452  0.01695
## poly(Olaparib, 3)3:GSIDWT siBRCA1       0.132049   0.104443   1.264  0.21070
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siCtrl  0.099978   0.104443   0.957  0.34204
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siCtrl -0.261718   0.104443  -2.506  0.01477
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siCtrl -0.118483   0.104443  -1.134  0.26085
##                                           
## (Intercept)                            ***
## poly(Olaparib, 3)1                     ***
## poly(Olaparib, 3)2                     .  
## poly(Olaparib, 3)3                     ** 
## GSIDWT siCtrl                          .  
## GSIDWT siBRCA1                         ***
## GSIDYFP-ALC1 siCtrl                    ** 
## poly(Olaparib, 3)1:GSIDWT siCtrl       *  
## poly(Olaparib, 3)2:GSIDWT siCtrl       ** 
## poly(Olaparib, 3)3:GSIDWT siCtrl       ** 
## poly(Olaparib, 3)1:GSIDWT siBRCA1      ***
## poly(Olaparib, 3)2:GSIDWT siBRCA1      *  
## poly(Olaparib, 3)3:GSIDWT siBRCA1         
## poly(Olaparib, 3)1:GSIDYFP-ALC1 siCtrl    
## poly(Olaparib, 3)2:GSIDYFP-ALC1 siCtrl *  
## poly(Olaparib, 3)3:GSIDYFP-ALC1 siCtrl    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03693 on 64 degrees of freedom
## Multiple R-squared:  0.9761, Adjusted R-squared:  0.9705 
## F-statistic: 174.2 on 15 and 64 DF,  p-value: < 2.2e-16
## 
## AIC:  -284.6344

# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in WT siCtrl	-1.67302641677882	0.0738525545555613	-22.653602530704	2.8675978456102e-32
Olaparib2 in WT siCtrl	-0.427341397226602	0.0738525545555613	-5.78641320937791	2.34155318762481e-07
Olaparib3 in WT siCtrl	-0.0942068961893334	0.0738525545555612	-1.27560782096521	0.206704814006636
Olaparib1: WT siCtrl vs. WT siBRCA1	-0.716369329272089	0.104443284268374	-6.85893146974709	3.28776419764364e-09
Olaparib2: WT siCtrl vs. WT siBRCA1	0.0270833519374022	0.104443284268374	0.259311569213103	0.796227144303063
Olaparib3: WT siCtrl vs. WT siBRCA1	0.458932815367303	0.104443284268374	4.39408640375618	4.27341835608061e-05
Olaparib1: WT siCtrl vs. YFP-ALC1 siCtrl	0.337956247565736	0.104443284268374	3.23578724982772	0.00192195401659994
Olaparib2: WT siCtrl vs. YFP-ALC1 siCtrl	0.0214521988635816	0.104443284268374	0.205395674923998	0.837914905229145
Olaparib3: WT siCtrl vs. YFP-ALC1 siCtrl	0.208400646267573	0.104443284268374	1.99534750106167	0.0502657020232333
Olaparib1: WT siCtrl vs. YFP-ALC1 siBRCA1	0.237977794106685	0.104443284268374	2.27853610477421	0.0260410108569245
Olaparib2: WT siCtrl vs. YFP-ALC1 siBRCA1	0.28316978677541	0.104443284268374	2.71123020267906	0.00859672135774766
Olaparib3: WT siCtrl vs. YFP-ALC1 siBRCA1	0.326883481576297	0.104443284268374	3.12977022760362	0.00263497283648194
	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in WT siBRCA1	-2.38939574605091	0.0738525545555614	-32.353596438608	2.22643817702738e-41
Olaparib2 in WT siBRCA1	-0.400258045289199	0.0738525545555612	-5.41969127131647	9.67428333899372e-07
Olaparib3 in WT siBRCA1	0.364725919177968	0.0738525545555612	4.938568765466	5.94074515631804e-06
Olaparib1: WT siBRCA1 vs. WT siCtrl	0.716369329272092	0.104443284268374	6.85893146974711	3.28776419764348e-09
Olaparib2: WT siBRCA1 vs. WT siCtrl	-0.0270833519374038	0.104443284268374	-0.259311569213119	0.796227144303051
Olaparib3: WT siBRCA1 vs. WT siCtrl	-0.458932815367302	0.104443284268374	-4.39408640375618	4.27341835608073e-05
Olaparib1: WT siBRCA1 vs. YFP-ALC1 siCtrl	1.05432557683783	0.104443284268374	10.0947187195748	7.26674272784559e-15
Olaparib2: WT siBRCA1 vs. YFP-ALC1 siCtrl	-0.00563115307382137	0.104443284268374	-0.0539158942891126	0.957170122922242
Olaparib3: WT siBRCA1 vs. YFP-ALC1 siCtrl	-0.250532169099729	0.104443284268374	-2.39873890269451	0.0193760572681773
Olaparib1: WT siBRCA1 vs. YFP-ALC1 siBRCA1	0.954347123378776	0.104443284268374	9.13746757452131	3.25723289421966e-13
Olaparib2: WT siBRCA1 vs. YFP-ALC1 siBRCA1	0.256086434838007	0.104443284268374	2.45191863346595	0.0169486519337061
Olaparib3: WT siBRCA1 vs. YFP-ALC1 siBRCA1	-0.132049333791005	0.104443284268374	-1.26431617615256	0.210701703928966
	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in YFP-ALC1 siCtrl	-1.33507016921308	0.0738525545555614	-18.0775083170437	7.76346316342059e-27
Olaparib2 in YFP-ALC1 siCtrl	-0.405889198363021	0.0738525545555613	-5.49593986024762	7.21927350491887e-07
Olaparib3 in YFP-ALC1 siCtrl	0.11419375007824	0.0738525545555613	1.54623967668348	0.126977607090077
Olaparib1: YFP-ALC1 siCtrl vs. WT siCtrl	-0.337956247565736	0.104443284268374	-3.23578724982772	0.00192195401659992
Olaparib2: YFP-ALC1 siCtrl vs. WT siCtrl	-0.021452198863581	0.104443284268374	-0.205395674923993	0.83791490522915
Olaparib3: YFP-ALC1 siCtrl vs. WT siCtrl	-0.208400646267573	0.104443284268374	-1.99534750106167	0.0502657020232333
Olaparib1: YFP-ALC1 siCtrl vs. WT siBRCA1	-1.05432557683782	0.104443284268374	-10.0947187195748	7.26674272784577e-15
Olaparib2: YFP-ALC1 siCtrl vs. WT siBRCA1	0.00563115307382026	0.104443284268374	0.053915894289102	0.957170122922251
Olaparib3: YFP-ALC1 siCtrl vs. WT siBRCA1	0.25053216909973	0.104443284268374	2.39873890269451	0.0193760572681773
Olaparib1: YFP-ALC1 siCtrl vs. YFP-ALC1 siBRCA1	-0.0999784534590503	0.104443284268374	-0.957251145053513	0.342042677310214
Olaparib2: YFP-ALC1 siCtrl vs. YFP-ALC1 siBRCA1	0.261717587911828	0.104443284268374	2.50583452775507	0.0147699995936779
Olaparib3: YFP-ALC1 siCtrl vs. YFP-ALC1 siBRCA1	0.118482835308724	0.104443284268374	1.13442272654194	0.260848428173972
	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in YFP-ALC1 siBRCA1	-1.43504862267213	0.0738525545555614	-19.4312658689755	1.54060130170662e-28
Olaparib2 in YFP-ALC1 siBRCA1	-0.144171610451193	0.0738525545555613	-1.95215468603363	0.0552975074530851
Olaparib3 in YFP-ALC1 siBRCA1	0.232676585386964	0.0738525545555613	3.15055568202336	0.00247812247691705
Olaparib1: YFP-ALC1 siBRCA1 vs. WT siCtrl	-0.237977794106686	0.104443284268374	-2.27853610477421	0.0260410108569244
Olaparib2: YFP-ALC1 siBRCA1 vs. WT siCtrl	-0.283169786775409	0.104443284268374	-2.71123020267906	0.00859672135774774
Olaparib3: YFP-ALC1 siBRCA1 vs. WT siCtrl	-0.326883481576297	0.104443284268374	-3.12977022760362	0.00263497283648194
Olaparib1: YFP-ALC1 siBRCA1 vs. WT siBRCA1	-0.954347123378775	0.104443284268374	-9.1374675745213	3.25723289421976e-13
Olaparib2: YFP-ALC1 siBRCA1 vs. WT siBRCA1	-0.256086434838007	0.104443284268374	-2.45191863346596	0.0169486519337059
Olaparib3: YFP-ALC1 siBRCA1 vs. WT siBRCA1	0.132049333791006	0.104443284268374	1.26431617615257	0.210701703928965
Olaparib1: YFP-ALC1 siBRCA1 vs. YFP-ALC1 siCtrl	0.0999784534590502	0.104443284268374	0.957251145053513	0.342042677310214
Olaparib2: YFP-ALC1 siBRCA1 vs. YFP-ALC1 siCtrl	-0.261717587911828	0.104443284268374	-2.50583452775506	0.014769999593678
Olaparib3: YFP-ALC1 siBRCA1 vs. YFP-ALC1 siCtrl	-0.118482835308724	0.104443284268374	-1.13442272654194	0.260848428173972

write.table(output, file = "Figure5G_Stats_New_All.txt", quote = F, sep = "\t", row.names = T, col.names = NA)